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FogProtect: Protecting Sensitive data in the computing Continuum 8th

FogProtect: Protecting Sensitive Data in the Computing Conti...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European conference on Service-Oriented and Cloud computing, ESOCC 2020

作者： Ayed, Dhouha Jaho, Eva Lachner, Clemens Mann, Zoltán Ádám Seidl, Robert Surridge, Mike Thales Paris France Athens Technology Center Athens Greece TU Wien Wien Austria University of Duisburg-Essen Essen Germany Nokia Bell Labs Munich Germany University of Southampton Southampton United Kingdom

ISBN: (纸本)9783030719050

computing resources are being moved towards the edge of the network, in the form of so-called fog nodes, providing benefits in terms of reduced latency, increased processing speed, data locality, and energy savings. data produced in end devices like smartphones, sensors or IoT devices can be stored, processed and analysed across a continuum of computing resources, from end devices via fog nodes to cloud services. data related to critical domains, such as healthcare, public surveillance or home automation, requires tailored data protection mechanisms, spanning the whole computing continuum. The FogProtect project aims to provide novel advanced technologies and methodologies to ensure end-to-end protection of such sensitive data. Our generic solutions facilitate the provisioning and usage of applications and services in the computing continuum, by combining four technology innovations: (1) secure data container technology for data portability and mobility, (2) data-protection-aware adaptive service and resource management, (3) advanced data protection policy management, (4) dynamic data protection risk management models and tools. The applicability and impact of those solutions is evaluated and demonstrated on three complementary real-world use cases in the area of (1) smart cities, (2) smart manufacturing, and (3) smart media. © 2021, Springer Nature Switzerland AG.

关键词： Energy conservation

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Metaverse in Education: Vision, Opportunities, and Challenges

Metaverse in Education: Vision, Opportunities, and Challenge...

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IEEE international conference on Big data

作者： Hong Lin Shicheng Wan Wensheng Gan Jiahui Chen Han-Chieh Chao Jinan University Guangzhou China Pazhou Lab Guangzhou China Guangdong University of Technology Guangzhou China National Dong Hwa University Hualien Taiwan R.O.C.

ISBN: (纸本)9781665480468

Traditional education has been updated with the development of information technology in human history. Within big data and cyber-physical systems, the Metaverse has generated strong interest in various applications (e.g., entertainment, business, and cultural travel) over the last decade. As a novel social work idea, the Metaverse consists of many kinds of technologies, e.g., big data, interaction, artificial intelligence, game design, Internet computing, Internet of Things, and blockchain. It is foreseeable that the usage of Metaverse will contribute to educational development. However, the architectures of the Metaverse in education are not yet mature enough. There are many questions we should address for the Metaverse in education. To this end, this paper aims to provide a systematic literature review of Metaverse in education. This paper is a comprehensive survey of the Metaverse in education, with a focus on current technologies, challenges, opportunities, and future directions. First, we present a brief overview of the Metaverse in education, as well as the motivation behind its integration. Then, we survey some important characteristics for the Metaverse in education, including the personal teaching environment and the personal learning environment. Next, we envisage what variations of this combination will bring to education in the future and discuss their strengths and weaknesses. We also review the state-of-the-art case studies (including technical companies and educational institutions) for Metaverse in education. Finally, we point out several challenges and issues in this promising area.

关键词： Systematics Metaverse Education Big data Blockchains Internet of Things History

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UAV Payload Detection Using Deep Learning and data Augmentation

UAV Payload Detection Using Deep Learning and Data Augmentat...

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IEEE international conference on Robotic computing (IRC)

作者： Ilmun Ku Seungyeon Roh Gyeongyeong Kim Charles Taylor Yaqin Wang Eric T Matson Artificial Intelligence Convergence Hankuk University of Foreign Studies Seoul South Korea Computer Science and Engineering Konkuk University Seoul South Korea Computer Science and Engineering Sunmoon University Asan South Korea Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9781665472616

In recent years, the technology behind Unmanned Aerial Vehicles (UAVs) has continually advanced. However, with these developments, malicious activities employing UAVs have also been on the rise. Within this study, Deep Learning (DL) algorithms are utilized to detect and classify UAVs transporting payload based on the sound they release. In order to exercise DL algorithms on a set of data, a sufficient amount of audio data is necessary to obtain a more reliable result. So UAV sound recordings have been collected alongside the use of data augmentation to secure a satisfactory sample size for testing purposes. Afterward, a feature-based classification was applied to the groups of audio identifying each UAV’s payload (or lack thereof). Lastly, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Convolutional Recurrent Neural Network(CRNN) are utilized in analyzing the final data-set. They are evaluated for their abilities to correctly categorize the unloaded, one payload, and two payload of UAV classes and noise class solely through audio. As a result, MFCC showed the best performance in CNN, RNN, and CRNN, which are 0.9493, 0.8133, and 0.9174 accuracies. Our contribution to this study is that a cost-efficient data collection method was applied by utilizing laptop microphones. Moreover, DL technology was used in UAV payload detection, whereas neural network was used in prior study. Also, the best feature for UAV payload detection with the three DL technologies was found. The limitation of the paper is that only two UAV models and one kind of payload were used to collect data. Diverse UAVs and payload are expected to be used to collect data in future works.

关键词： Deep learning Recurrent neural networks Autonomous aerial vehicles Classification algorithms Recording Convolutional neural networks Reliability

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Expression of Concern for: Text Classification Using Feature Extraction and Classification Model

Expression of Concern for: Text Classification Using Feature...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anirudh Rana Vishal Bharti Department Of Computer Science and Engineering Chandigarh University Gharuan

ISBN: (数字)9781665474368

ISBN: (纸本)9781665474375

Sentiment analysis represents context-based text mining that classifies and abstracts particular information from the source data, and helps to interpret people's feelings. It adopts the NLP approach to classify people's opinions regarding goods or feedback. Sentiment analysis handles people's opinion and behavior in the context of feelings and attitudes concerning an episode or incidence. There are many domains that use opinion mining widely such as commercial goods review, social media study and film evaluation and so on. Semantic analysis provides major support in building recommendation frameworks. User provides textual feedback such as web-based reviews, remarks or responses on social networking and business sites. Machine learning is a common approach used for classification. This project devises a nascent methodology through the integration of dissimilar frameworks. This work applies Random Forest as a feature extraction method for taking out crucial features of the data suite. The K-mean clustering is enforced which can cluster similar type of features and in the last voting classifier is applied. Voting classifier consists of LR, KNN, SVM models. Python is the tool used in this work for the implementation of the devised methodology and evaluates new methodology with respect to some global performance indices.

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Research on key technologies of net metering for smart grid AMI and data mining of user electricity consumption

Research on key technologies of net metering for smart grid ...

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Electronics and Devices, Computational science (ICEDCS), international conference on

作者： Ji Li Linghua Kong Chao Cheng Jianpo Dong State Grid Beijing Electric Power Company Beijing China China GRIDCOM CO. Ltd Shenzhen China

ISBN: (数字)9781665455411

ISBN: (纸本)9781665455428

The accurate measurement of users' electricity consumption and the mining of relevant data are essential links in power companies' production and operation management and the stable economic operation of power grids. The accuracy of measurement and quality reliability directly affect the economic interests of users and social energy utilization. How to accurately distinguish measurement anomalies caused by user behavior is a technical problem to be solved. In recent years, due to the promotion of intelligent consumer electricity meters and the vast improvement of the construction of electricity consumption information collection systems, power grid companies have accumulated a large number of consumer electricity consumption data. Based on this, this paper proposes a neural network algorithm based on particle swarm optimization. Experiments show that the method has high feasibility and reliability and can be promoted.

关键词： Economics Power measurement Scientific computing Companies Production Behavioral sciences Smart grids

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Integrity Auditing for Secure Cloud Storage on Sensitive data Protection

Integrity Auditing for Secure Cloud Storage on Sensitive Dat...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Janaki Sivakumar Mazhar Malik Arun Sekar Rajasekaran Department of Computing and IT (CIT) Global College of Engineering and Technology CPO Ruwi 112 Muscat Oman School of Computer Science and Creative Technologies University of the West of England Bristol United Kingdom Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9781665491129

Users can interchange data with others and remotely store their data on the cloud using cloud storage services. The integrity of data saved in the cloud should be ensured through remote data integrity audits. An electronic health record system is one popular example of a well-known cloud storage system where a cloud file may include unique sensitive data. When a cloud file is shared, private information shouldn’t be exposed. Sensitive information can be realized if the entire shared file is encrypted, but other users cannot access it. How to share sensitive material that is hidden in remote data integrity audits is still up in the air. In this method, data blocks that match sensitive information in the file are sanitized, and the data block signatures are changed to ones that are appropriate for the cleaned-up file. Throughout the integrity auditing process, these signatures are used to verify the accuracy of the cleaned file. Since sensitive information is concealed, our solution permits the sharing and usage of a cloud-stored file by others while maintaining the capacity to conduct remote data integrity audits efficiently.

关键词： Wireless communication Cloud computing data integrity data protection Cryptography Electronic medical records Secure storage

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Predictive analytics for cardiovascular disease diagnosis using machine learning techniques 5th

Predictive analytics for cardiovascular disease diagnosis us...

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5th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2020

作者： Muniasamy, Anandhavalli Muniasamy, Vasanthi Bhatnagar, Roheet College of Computer Science King Khalid University Abha Saudi Arabia Department of CSE Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811533822

Medical data can be mined for effective decision making in the presence of disease analysis. Globally, cardiovascular alias heart disease is one of the highly rated causes of death [1] disease which will lead to 76% of the deaths [2] by the year 2030. Currently, the techniques of machine learning and predictive analytics have proven importance in medical data analysis. In a nutshell, this paper aims to apply six classifiers namely artificial neural network, support vector machine, decision tree, nearest neighbor, linear discriminant analysis, random forest, and to predict the presence of heart diseases in the patient’s datasets. Moreover, the performance of these classifiers on three heart disease datasets is compared. The results reveal that RF, LDA, DT, and ANN have performed better than SVM and KNN in terms of accuracy, recall, F1-score, confusion matrix, and error rate. © Springer Nature Singapore Pte Ltd 2021.

关键词： Support vector machines

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A framework for selecting classification models in the intruder detection system using topsis 3rd

A framework for selecting classification models in the intru...

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3rd international conference on Human Interaction and Emerging technologies: Future Applications, IHIET 2020

作者： Martinez, Miguel Angel Quiroz Rugel, Deivid Temistocles Leon Alcivar, Carlos Jose Espinoza Vazquez, Maikel Yelandi Leyva Department Computer Science Salesian Polytechnic University Guayaquil Ecuador

ISBN: (纸本)9783030553067

As the network has expanded considerably, security mechanisms are a key issue in networks. Intrusive activities, such as unauthorized access and data manipulation, are increasing. Therefore, the role of the Network Intrusion Detection System (NIDS) in monitoring network traffic for activity and determining whether an intrusion has occurred is very important. The performance of an IDS depends on the selection of the classification model and training data, however, many classifiers generate similar results when measuring performance. The technique of order of preference for similarity to the ideal solution (TOPSIS) is used to select one or more alternatives based on the criteria. The main objective is to present some classification models used in a data set to select the best alternative according to the performance criteria using the TOPSIS method. The deductive method and selection research technique were applied to study the NSL-KDD. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Machine learning

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A Smart Approach using Multi–agent System for Big data Security

A Smart Approach using Multi–agent System for Big Data Secu...

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international conference on Internet of Things: Systems, Management and Security

作者： Dounya Kassimi Okba Kazar Ezedin Barka Abdelhak Merizig Zina Houhamdi Belkacem Athamena Meftah Zaoui Department of Computer Science University Center Aflou Laghouat Algeria Department of Information Systems and Security UAE University Al Ain United Arab Emirates Department of Computer Science University of Biskra Biskra Algeria Cybersecurity Department Al Ain University Al Ain United Arab Emirates Business Administration Department Al Ain University Al Ain United Arab Emirates

With the evaluation of technology and the appearance of new tools that help store the information we create, especially in Banking, business intelligence, and even Education as datawarehouse, Big data, and cloud computing. Those new tools create another obstacle: how we can secure and protect the information and the data stored in them. This paper treats the problem of Big data security and privacy using mobile and stationary agents’ technologies. The main important security proprieties in big data are integrity, authentication, privacy, and access control. For integrity, the problem lies in checking the integrity of data and if this data is good and can be used since the Big data is receiving data from different sours and different formats (structured, semi–structured, and non-structured). In access control, we need to consider the users’ secrecy, monitor the authorities, and properly apply the confidentiality requirement. The problem with authentication is to authenticate each user over the network, while big data collect sensitive data from trusted or untrusted users. While the privacy policy problem revolves around data collection and the use of transparency. To answer the previous needs for security in big data, we have proposed a smart approach using multi–agent systems (MAS) as a model for our solution.

关键词： Access control data privacy Cloud computing Authentication Intrusion detection Big data Safety

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Synthesis of Aromatic Hyperbranched Polymer based on Diphenolic Acid and Pentaerythritol: Reaction Kinetics using FTIR technique 1

Synthesis of Aromatic Hyperbranched Polymer based on Dipheno...

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1st international conference on technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Gunasekhar, R. Dhevi, D. Manjula Sathiyanathan, P. Prabu, A. Anand Department of Chemistry School of Advanced Sciences Vellore Institute of Technology Vellore632014 India Department of Chemistry Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Fujian Institute of Research The Structure of Matter Chinese Academy of Sciences Fujian Fuzhou350002 China

ISBN: (纸本)9781607685395

Hyperbranched polymers (HBPs) are a common type of soft nanomaterial, but creating them from readily available monomers with a well-defined dendritic structure is difficult. advances in the quantitative description and control of hyperbranched fine topological structures will aid their development and application in a variety of fields, including traditional polymer materials and engineering as well as energy materials, and optoelectronic materials and devices. Using a sequential polycondensation technique based on commercially accessible monomers, we present a one-pot synthesis of aromatic HBP with configurable structure and high degree of branching. Generation 1 (G1) aromatic HBP (***) based on diphenolic acid and pentaerythritol has been synthesized in the presence of p-toluene sulfonic acid as catalyst. Kinetics reaction was monitored using FTIR spectroscopy. -C=O absorption band of ester was used to track the conversion of acid to ester. The reaction of ***-G1 synthesis takes approximately 21 h for completion according to kinetic parameters computed from IR spectral data. © The Electrochemical Society

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